Time series are data observed over time (either in continuous time or at discrete time periods).

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Multivariate time series regressions in R

I've never done time series analysis before and I was hoping to get some tips/steps on how to go about doing so. Outcome: n=39, t=60 multivariate time series of monthly sales of n goods ...
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How to fit a logistic regression model for time series data?

Suppose we have binary response variable $Y \in \{1, 0\}$, and some covariates $\mathbf{X}= (x_1, \ldots, x_p)'$. Suppose also that we can observe the values of $(y, \mathbf{x})$ at each time point $t ...
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Holt-Winters Forecasting - Why do we use most recent estimate for all projections going forward?

Ive been doing some research on using the Holt-Winters method for forecasting and understand all but one aspect. Why do we use the most recent estimate for the base and trend components for all ...
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62 views

Modelling time varying volatility when GARCH(1,1) coefficients sum to value greater one

First of all I have to admit that I am not a time-series expert so any help is highly appreciated. I have a financial time-series (a fixed income total return index measured on a weekly basis) for ...
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57 views

Intervention analysis and forecasting in this type of data

I am working on a project where I am to do the intervention analysis and forecasting based on the time series. The problem is something like: I have a normal time series entries but in between them ...
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16 views

Linear Model learnt on several frequency

Sorry for the unclear title but I did not know how to call this POST, I'll try to be clearer. I have data that I need to forecast with external variables. This data is a time series available on a ...
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1answer
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Is OLS in Engle-Granger a valid method to use when finding the cointegrating vector?

In this post mpiktas showed that the sample correlation measure for two random walks (possible correlated) is a random variable and does not estimate the theoretical correlation. When trying to find a ...
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45 views

Estimating ARCH model using ML or OLS

ARCH(p) models are defined as: $σ^2 = a_0 + ∑a_ie^2_{t-i}+e_t$, $i>0$ Now, as with any VMA model, estimating this model using OLS/ML is impossible, because the error term is not observable. But ...
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Clustering timestamped data

hi I have data timestamped based on entry and exit of several people in the same house the problem is not to classify these people but to actually make a clustering on the variables input / output to ...
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Getting exactly same forecasted values in auto arima [duplicate]

I am using auto.arima from forecast package for time series. The auto.arima selects best ...
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25 views

Interpreting a SARIMA model in SPSS - when is the model “good” enough for Interrupted Time Series

I am trying to analyse time trend data across a 10 year period (monthly) using SPSS, to do an interrupted time series analysis. I am not sure however, when a seasonal ARIMA model is "good enough". For ...
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Is Distributed Lag and Moving Average the same thing?

I am currently reading Principle of Econometrics by Hill, Griffith and Lim, in the part on time series, they use the term Distributed Lag (DL) , and ARDL(1,1) etc. So is DL the same thing as Moving ...
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Evaluating parameters of a time series model on multiple experimental sessions

I'm trying to evaluate a model for a time series, given many time series (plural). For example, i'm using the forecast package and in particular the ...
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59 views

Why include a time trend in a regression?

I have a regression model of unemployment on vacancy over 20 years. The model also includes a time trend time trend = 0, 1, 2 , 3 ,.... What's the reason for including a time trend? Thank you
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1answer
37 views

Breusch-Godfrey Test for autocorrelation

Following the steps of Breusch–Godfrey test , I wrote my own R code which differs from the R function for ...
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Making forecasts in minitab only positive

I'm looking at 30 years of rainfall data, working with the daily average rainfall per month (That sounds weird... it's the total rainfall in the month, divided by number of days in the month). ...
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235 views

Examples of Non-Linear Time Series?

Does anyone have an example of real world (ideally multivariate) time-series data that depends on its past in a non-linear, but additive way? I understand that there are several examples of ...
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69 views

Reproducing Pankratz's ARIMA models for U.S. savings rate

I'm wondering if anyone has been able to reproduce the U.S. savings rate models in Pankratz's "Forecasting with Dynamic Regression Models"? If you google: pankratz table-1.6 The first link for me ...
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Estimating static factor model for h-step ahead forecasting (using R)

I am trying to estimate a static factor model of the following form $$y_{t+1} = \beta'F_t+\gamma(L)y_t+\epsilon_{t+1}, \\ X_t=\Lambda F_t+e_t$$ where $F_t=(f'_t,...,f'_{t-q})'$ is $r\times 1$, where ...
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Change over time of statistical samples

Given a large distribution of data with high variation, I want to measure the average impact of any input that modify the data (in one direction only, for example make them increasing). What should I ...
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Aggregate ts data with few observations

I am looking to predict the outcome of a business process (inherently complex). I have four years of data across 15 companies. I am trying to: (1) understand key drivers in this process (what ...
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t statistic for regression model with (possible) autocorrelation and heteroscelasticity

Looking at other questions at CrossValidated helped me so many times over the last weeks - so thank you guys! However for this question I was unable to find an answer or at least wasn't sure if ...
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State space model with intercept in transition equation and h-step forecast - FKF R

I think I found the solution myself but would need some verification by an expert. To see my solution you can skip the start and switch to the end of my question. My problem is now: How do I get a ...
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71 views

Online mean shift algorithms

I am looking for an online algorithm which can identify mean shifting in a time series quickly, I have seen some algorithms that do so but they require 50+ samples in order to flag that the mean has ...
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1answer
37 views

Volatility clustering test in Stata, time frame issues

I tested AAPL for ARCH effects using Stata with two different datasets (one was a subset of the other) and I obtained significant arch effects in one subset but not in the whole dataset. I would ...
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Binomial Temporal GAMM does not converge (R::mgcv)

I am new to both mixed effect and Additive models so I'm sorry if the answer here is trivial. I have data collected on several metabolic chemicals (M1,M2...), covariates (time,Race,Gender...) and ...
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99 views

How to detect peaks and trends within time series data?

I've been trying to detect defrost cycles using refrigeration data. I have a sensor within the fridge which simulates a product and measures the temperature every 15 minutes. Usually, based on the ...
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What does the following ADF test in R signify?

While checking for stationarity of differences (first difference) data using ADF test in R, I get that the test statistic is significant for all the deterministic regressors - "none", "drift", ...
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36 views

How are the weights set in the AR and MA?

I am studying ARIMA models and trying to get a grip of the concept. But I cant seem to find anyone motivating the values on the weights. Consider the following AR(2) model below, $Y_{t} = \mu + ...
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Exponential Smoothing with Causal Regressors

I am trying to develop several approaches to analyze the effect of covariates on retail sales . the first approach i am trying to use is exponential smoothing with regressors (for its simplicity to ...
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62 views

Testing Sharpe Ratio significance

What is the proper way to test the significance of Sharpe Ratios or Information Ratios? The Sharpe Ratios will be based on various equity indices and may have variable look-back periods. One solution ...
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31 views

Multicollinearity in levels and changes (regression analysis)

I am wondering, is it possible that when conducting a multiple regression analysis by using levels, there is a relatively high multicollinearity (over 75%) but when having all the variables in ...
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55 views

Help needed with correlating two datasets

First cross-validated question so please be gentle :o) I have two datasets all gathered and managed in 'R'... Dataset 1 - News Corpus. Contains 3,270 entries from the period 1/Apr/13 to 31/Mar/14. ...
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How to quantify the impact of a variable in a VAR model equation?

Given a VAR model (the equations that make the model, coefficient significance and the adjusted $R^2$ value for each equation), is there a way to calculate the impact of a variable over the other?
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Decomposing time series with 2 trends

I have some web data from 2011 to present, and I would like to look at its seasonality in R. However, in June 2014, a change to the website was made that boosted the numbers by a lot, throwing off my ...
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Tests for imputed time-series dataset

I am currently dealing with serial measurements, changes of some parameter in patients over time. Values are mostly annual, distance between samples for each person is about a year. I want to perform ...
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Diferencing an autoregressive model

By differencing an AR(1) Model could we get an MA(1)? I mean $Y_t = U + Y_{t-1} + e_t$ $\Delta Y_t = Y_t - Y_{t-1} = U + E_t = U + E_t + 0 * E_{t-1}$ >> Meaning MA(1) ?
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Covariance estimation of overlapping time series

I have two series $x_t,z_t$, and compute the differences like $\Delta_h x_t=x_t-x_{t-h}$. What is a good estimator of the covariance of changes? $$Cov[\Delta_h x_t,\Delta_h z_t]$$ The intervals are ...
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Constructing 95% confidence intervals of coefficients in ARIMA model

For the recurrence relation below I simulated it in R with arima.sim and used arima.sim(data, order=c(2,0,2)) to estimate the coefficients. $x_t = x_{t-1} - \frac 13 x_{t-2} + w_t + \frac 14 ...
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Why the correlatio=corARMA in gamm are not useful to control the autocorrelation

I want to analyze the climate effects on the number of malair (Frequency) in several cities(region), while I find there is serious autocorrelation in residuals. However, there seems to be no ...
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Estimate VAR model from data about lags

Does anybody have any idea how i would write the var model based on this table? What coefficients should be included? Any hint will be much appreciated. Thank you!
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42 views

Estimate UCM Equation from Ucm model?

Given the output of a ucm sas procedure i need to estimate the equations from the given output , but i don't really know how to do it or where to start. Do you have any hints or anything that might ...
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Time Series data used for estimation

I am new to Time Series analysis, but I have Time Series Data. I am trying to estimate the total number of users of a product that occurred over a three month period. I have one observation of the ...
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52 views

How to calculate the cross correlation between two time series measured at different instants?

I have two time series with measurements of the same type but different stations. I would like to know if the two series are correlated and how much is the "lag" between them. The idea is that in this ...
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31 views

Identifying deterministic trend vs stochastic trend

In EViews for Augmented Dickey Fuller Test I get a p value of 0.4326 .I have few questions regarding this? 1.Does this mean I have a stochastic trend or a ...
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How to write ar & ma terms in dynamic regression/arimax in terms of actual predictors?

I have done Arimax with response series Y as sales/demand & a set of input series on time series data at monthly level. The estimates from the arimax model is as shown below. I want to now ...
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What to look for in vector time series, and how

In univariate time series, it's pretty intuitive to look for transformations to give you a stationary covariance noise component, e.g., linear or seasonal trends. And you can look at the ...
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Is there an advantage to using moving average versus removing outliers?

I have a dataset and for each hour there is 3 readings (sometimes missing and sometimes clearly an outlier). I am trying to find the mean of the entire dataset for the parameter. It has been suggested ...
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State-space model: measurement-driven steps?

I have a time series that seems to be well described by a univariate local level model (a changing bias in human visual perception, sampled at regular intervals). I have a hunch, however, that the ...
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How to choose the order of a GARCH model?

In order to model time series with GARCH models in R, you first determine the AR order and the MA order using ACF and PACF plots. But then how do you determine the order of the actual GARCH model? ...